from dataclasses import dataclass from breed_health_info import breed_health_info from breed_noise_info import breed_noise_info import traceback import math @dataclass class UserPreferences: """使用者偏好設定的資料結構""" living_space: str # "apartment", "house_small", "house_large" yard_access: str # "no_yard", "shared_yard", "private_yard" exercise_time: int # minutes per day exercise_type: str # "light_walks", "moderate_activity", "active_training" grooming_commitment: str # "low", "medium", "high" experience_level: str # "beginner", "intermediate", "advanced" time_availability: str # "limited", "moderate", "flexible" has_children: bool children_age: str # "toddler", "school_age", "teenager" noise_tolerance: str # "low", "medium", "high" space_for_play: bool other_pets: bool climate: str # "cold", "moderate", "hot" health_sensitivity: str = "medium" barking_acceptance: str = None training_commitment: str = "medium" # "low", "medium", "high" - 訓練投入程度 living_environment: str = "ground_floor" # "ground_floor", "with_elevator", "walk_up" - 居住環境細節 def __post_init__(self): """在初始化後運行，用於設置派生值""" if self.barking_acceptance is None: self.barking_acceptance = self.noise_tolerance @staticmethod def calculate_breed_bonus(breed_info: dict, user_prefs: 'UserPreferences') -> float: """計算品種額外加分""" bonus = 0.0 temperament = breed_info.get('Temperament', '').lower() # 1. 壽命加分（最高0.05） try: lifespan = breed_info.get('Lifespan', '10-12 years') years = [int(x) for x in lifespan.split('-')[0].split()[0:1]] longevity_bonus = min(0.05, (max(years) - 10) * 0.01) bonus += longevity_bonus except: pass # 2. 性格特徵加分（最高0.15） positive_traits = { 'friendly': 0.05, 'gentle': 0.05, 'patient': 0.05, 'intelligent': 0.04, 'adaptable': 0.04, 'affectionate': 0.04, 'easy-going': 0.03, 'calm': 0.03 } negative_traits = { 'aggressive': -0.08, 'stubborn': -0.06, 'dominant': -0.06, 'aloof': -0.04, 'nervous': -0.05, 'protective': -0.04 } personality_score = sum(value for trait, value in positive_traits.items() if trait in temperament) personality_score += sum(value for trait, value in negative_traits.items() if trait in temperament) bonus += max(-0.15, min(0.15, personality_score)) # 3. 適應性加分（最高0.1） adaptability_bonus = 0.0 if breed_info.get('Size') == "Small" and user_prefs.living_space == "apartment": adaptability_bonus += 0.05 if 'adaptable' in temperament or 'versatile' in temperament: adaptability_bonus += 0.05 bonus += min(0.1, adaptability_bonus) # 4. 家庭相容性（最高0.1） if user_prefs.has_children: family_traits = { 'good with children': 0.06, 'patient': 0.05, 'gentle': 0.05, 'tolerant': 0.04, 'playful': 0.03 } unfriendly_traits = { 'aggressive': -0.08, 'nervous': -0.07, 'protective': -0.06, 'territorial': -0.05 } # 年齡評估這樣能更細緻 age_adjustments = { 'toddler': {'bonus_mult': 0.7, 'penalty_mult': 1.3}, 'school_age': {'bonus_mult': 1.0, 'penalty_mult': 1.0}, 'teenager': {'bonus_mult': 1.2, 'penalty_mult': 0.8} } adj = age_adjustments.get(user_prefs.children_age, {'bonus_mult': 1.0, 'penalty_mult': 1.0}) family_bonus = sum(value for trait, value in family_traits.items() if trait in temperament) * adj['bonus_mult'] family_penalty = sum(value for trait, value in unfriendly_traits.items() if trait in temperament) * adj['penalty_mult'] bonus += min(0.15, max(-0.2, family_bonus + family_penalty)) # 5. 專門技能加分（最高0.1） skill_bonus = 0.0 special_abilities = { 'working': 0.03, 'herding': 0.03, 'hunting': 0.03, 'tracking': 0.03, 'agility': 0.02 } for ability, value in special_abilities.items(): if ability in temperament.lower(): skill_bonus += value bonus += min(0.1, skill_bonus) # 6. 適應性評估 - 根據具體環境給予更細緻的評分 adaptability_bonus = 0.0 if breed_info.get('Size') == "Small" and user_prefs.living_space == "apartment": adaptability_bonus += 0.08 # 小型犬更適合公寓 # 環境適應性評估 if 'adaptable' in temperament or 'versatile' in temperament: if user_prefs.living_space == "apartment": adaptability_bonus += 0.10 # 適應性在公寓環境更重要 else: adaptability_bonus += 0.05 # 其他環境仍有加分 # 氣候適應性 description = breed_info.get('Description', '').lower() climate = user_prefs.climate if climate == 'hot': if 'heat tolerant' in description or 'warm climate' in description: adaptability_bonus += 0.08 elif 'thick coat' in description or 'cold climate' in description: adaptability_bonus -= 0.10 elif climate == 'cold': if 'thick coat' in description or 'cold climate' in description: adaptability_bonus += 0.08 elif 'heat tolerant' in description or 'short coat' in description: adaptability_bonus -= 0.10 bonus += min(0.15, adaptability_bonus) return min(0.5, max(-0.25, bonus)) @staticmethod def calculate_additional_factors(breed_info: dict, user_prefs: 'UserPreferences') -> dict: """ 計算額外的評估因素，結合品種特性與使用者需求的全面評估系統 此函數整合了： 1. 多功能性評估 - 品種的多樣化能力 2. 訓練性評估 - 學習和服從能力 3. 能量水平評估 - 活力和運動需求 4. 美容需求評估 - 護理和維護需求 5. 社交需求評估 - 與人互動的需求程度 6. 氣候適應性 - 對環境的適應能力 7. 運動類型匹配 - 與使用者運動習慣的契合度 8. 生活方式適配 - 與使用者日常生活的匹配度 """ factors = { 'versatility': 0.0, # 多功能性 'trainability': 0.0, # 可訓練度 'energy_level': 0.0, # 能量水平 'grooming_needs': 0.0, # 美容需求 'social_needs': 0.0, # 社交需求 'weather_adaptability': 0.0,# 氣候適應性 'exercise_match': 0.0, # 運動匹配度 'lifestyle_fit': 0.0 # 生活方式適配度 } temperament = breed_info.get('Temperament', '').lower() description = breed_info.get('Description', '').lower() size = breed_info.get('Size', 'Medium') # 1. 多功能性評估 - 加強品種用途評估 versatile_traits = { 'intelligent': 0.25, 'adaptable': 0.25, 'trainable': 0.20, 'athletic': 0.15, 'versatile': 0.15 } working_roles = { 'working': 0.20, 'herding': 0.15, 'hunting': 0.15, 'sporting': 0.15, 'companion': 0.10 } # 計算特質分數 trait_score = sum(value for trait, value in versatile_traits.items() if trait in temperament) # 計算角色分數 role_score = sum(value for role, value in working_roles.items() if role in description) # 根據使用者需求調整多功能性評分 purpose_traits = { 'light_walks': ['calm', 'gentle', 'easy-going'], 'moderate_activity': ['adaptable', 'balanced', 'versatile'], 'active_training': ['intelligent', 'trainable', 'working'] } if user_prefs.exercise_type in purpose_traits: matching_traits = sum(1 for trait in purpose_traits[user_prefs.exercise_type] if trait in temperament) trait_score += matching_traits * 0.15 factors['versatility'] = min(1.0, trait_score + role_score) # 2. 訓練性評估 - 考慮使用者經驗 trainable_traits = { 'intelligent': 0.3, 'eager to please': 0.3, 'trainable': 0.2, 'quick learner': 0.2, 'obedient': 0.2 } base_trainability = sum(value for trait, value in trainable_traits.items() if trait in temperament) # 根據使用者經驗調整訓練性評分 experience_multipliers = { 'beginner': 1.2, # 新手更需要容易訓練的狗 'intermediate': 1.0, 'advanced': 0.8 # 專家能處理較難訓練的狗 } factors['trainability'] = min(1.0, base_trainability * experience_multipliers.get(user_prefs.experience_level, 1.0)) # 3. 能量水平評估 - 強化運動需求匹配 exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper() energy_levels = { 'VERY HIGH': { 'score': 1.0, 'min_exercise': 120, 'ideal_exercise': 150 }, 'HIGH': { 'score': 0.8, 'min_exercise': 90, 'ideal_exercise': 120 }, 'MODERATE': { 'score': 0.6, 'min_exercise': 60, 'ideal_exercise': 90 }, 'LOW': { 'score': 0.4, 'min_exercise': 30, 'ideal_exercise': 60 } } breed_energy = energy_levels.get(exercise_needs, energy_levels['MODERATE']) # 計算運動時間匹配度 if user_prefs.exercise_time >= breed_energy['ideal_exercise']: energy_score = breed_energy['score'] else: # 如果運動時間不足，按比例降低分數 deficit_ratio = max(0.4, user_prefs.exercise_time / breed_energy['ideal_exercise']) energy_score = breed_energy['score'] * deficit_ratio factors['energy_level'] = energy_score # 4. 美容需求評估 - 加入更多毛髮類型考量 grooming_needs = breed_info.get('Grooming Needs', 'MODERATE').upper() grooming_levels = { 'HIGH': 1.0, 'MODERATE': 0.6, 'LOW': 0.3 } # 特殊毛髮類型評估 coat_adjustments = 0 if 'long coat' in description: coat_adjustments += 0.2 if 'double coat' in description: coat_adjustments += 0.15 if 'curly' in description: coat_adjustments += 0.15 # 根據使用者承諾度調整 commitment_multipliers = { 'low': 1.5, # 低承諾度時加重美容需求的影響 'medium': 1.0, 'high': 0.8 # 高承諾度時降低美容需求的影響 } base_grooming = grooming_levels.get(grooming_needs, 0.6) + coat_adjustments factors['grooming_needs'] = min(1.0, base_grooming * commitment_multipliers.get(user_prefs.grooming_commitment, 1.0)) # 5. 社交需求評估 - 加強家庭情況考量 social_traits = { 'friendly': 0.25, 'social': 0.25, 'affectionate': 0.20, 'people-oriented': 0.20 } antisocial_traits = { 'independent': -0.20, 'aloof': -0.20, 'reserved': -0.15 } social_score = sum(value for trait, value in social_traits.items() if trait in temperament) antisocial_score = sum(value for trait, value in antisocial_traits.items() if trait in temperament) # 家庭情況調整 if user_prefs.has_children: child_friendly_bonus = 0.2 if 'good with children' in temperament else 0 social_score += child_friendly_bonus factors['social_needs'] = min(1.0, max(0.0, social_score + antisocial_score)) # 6. 氣候適應性評估 - 更細緻的環境適應評估 climate_traits = { 'cold': { 'positive': ['thick coat', 'winter', 'cold climate'], 'negative': ['short coat', 'heat sensitive'] }, 'hot': { 'positive': ['short coat', 'heat tolerant', 'warm climate'], 'negative': ['thick coat', 'cold climate'] }, 'moderate': { 'positive': ['adaptable', 'all climate'], 'negative': [] } } climate_score = 0.4 # 基礎分數 if user_prefs.climate in climate_traits: # 正面特質加分 climate_score += sum(0.2 for term in climate_traits[user_prefs.climate]['positive'] if term in description) # 負面特質減分 climate_score -= sum(0.2 for term in climate_traits[user_prefs.climate]['negative'] if term in description) factors['weather_adaptability'] = min(1.0, max(0.0, climate_score)) # 7. 運動類型匹配評估 exercise_type_traits = { 'light_walks': ['calm', 'gentle'], 'moderate_activity': ['adaptable', 'balanced'], 'active_training': ['athletic', 'energetic'] } if user_prefs.exercise_type in exercise_type_traits: match_score = sum(0.25 for trait in exercise_type_traits[user_prefs.exercise_type] if trait in temperament) factors['exercise_match'] = min(1.0, match_score + 0.5) # 基礎分0.5 # 8. 生活方式適配評估 lifestyle_score = 0.5 # 基礎分數 # 空間適配 if user_prefs.living_space == 'apartment': if size == 'Small': lifestyle_score += 0.2 elif size == 'Large': lifestyle_score -= 0.2 elif user_prefs.living_space == 'house_large': if size in ['Large', 'Giant']: lifestyle_score += 0.2 # 時間可用性適配 time_availability_bonus = { 'limited': -0.1, 'moderate': 0, 'flexible': 0.1 } lifestyle_score += time_availability_bonus.get(user_prefs.time_availability, 0) factors['lifestyle_fit'] = min(1.0, max(0.0, lifestyle_score)) return factors def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences) -> dict: """計算品種與使用者條件的相容性分數的優化版本""" try: print(f"Processing breed: {breed_info.get('Breed', 'Unknown')}") print(f"Breed info keys: {breed_info.keys()}") if 'Size' not in breed_info: print("Missing Size information") raise KeyError("Size information missing") def calculate_space_score(size: str, living_space: str, has_yard: bool, exercise_needs: str) -> float: """ 主要改進： 1. 更均衡的基礎分數分配 2. 更細緻的空間需求評估 3. 強化運動需求與空間的關聯性 """ # 重新設計基礎分數矩陣，降低普遍分數以增加區別度 base_scores = { "Small": { "apartment": 0.90, # 降低滿分機會 "house_small": 0.85, # 小型犬不應在大空間得到太高分數 "house_large": 0.80 # 避免小型犬總是得到最高分 }, "Medium": { "apartment": 0.40, # 維持對公寓環境的限制 "house_small": 0.80, # 適中的分數 "house_large": 0.90 # 給予合理的獎勵 }, "Large": { "apartment": 0.10, # 加重對大型犬在公寓的限制 "house_small": 0.60, # 中等適合度 "house_large": 0.95 # 最適合的環境 }, "Giant": { "apartment": 0.10, # 更嚴格的限制 "house_small": 0.45, # 顯著的空間限制 "house_large": 0.95 # 最理想的配對 } } # 取得基礎分數 base_score = base_scores.get(size, base_scores["Medium"])[living_space] # 運動需求相關的調整更加動態 exercise_adjustments = { "Very High": { "apartment": -0.25, # 加重在受限空間的懲罰 "house_small": -0.15, "house_large": -0.05 }, "High": { "apartment": -0.20, "house_small": -0.10, "house_large": 0 }, "Moderate": { "apartment": -0.10, "house_small": -0.05, "house_large": 0 }, "Low": { "apartment": 0.05, # 低運動需求在小空間反而有優勢 "house_small": 0, "house_large": -0.05 # 輕微降低評分，因為空間可能過大 } } # 根據空間類型獲取運動需求調整 adjustment = exercise_adjustments.get(exercise_needs, exercise_adjustments["Moderate"])[living_space] # 院子效益根據品種大小和運動需求動態調整 if has_yard: yard_bonus = { "Giant": 0.20, "Large": 0.15, "Medium": 0.10, "Small": 0.05 }.get(size, 0.10) # 運動需求會影響院子的重要性 if exercise_needs in ["Very High", "High"]: yard_bonus *= 1.2 elif exercise_needs == "Low": yard_bonus *= 0.8 current_score = base_score + adjustment + yard_bonus else: current_score = base_score + adjustment # 確保分數在合理範圍內，但避免極端值 return min(0.95, max(0.15, current_score)) def calculate_exercise_score(breed_needs: str, exercise_time: int, exercise_type: str) -> float: """ 精確評估品種運動需求與使用者運動條件的匹配度 Parameters: breed_needs: 品種的運動需求等級 exercise_time: 使用者能提供的運動時間（分鐘） exercise_type: 使用者偏好的運動類型 Returns: float: -0.2 到 0.2 之間的匹配分數 """ # 定義更細緻的運動需求等級 exercise_levels = { 'VERY HIGH': { 'min': 120, 'ideal': 150, 'max': 180, 'intensity': 'high', 'sessions': 'multiple', 'preferred_types': ['active_training', 'intensive_exercise'] }, 'HIGH': { 'min': 90, 'ideal': 120, 'max': 150, 'intensity': 'moderate_high', 'sessions': 'multiple', 'preferred_types': ['active_training', 'moderate_activity'] }, 'MODERATE HIGH': { 'min': 70, 'ideal': 90, 'max': 120, 'intensity': 'moderate', 'sessions': 'flexible', 'preferred_types': ['moderate_activity', 'active_training'] }, 'MODERATE': { 'min': 45, 'ideal': 60, 'max': 90, 'intensity': 'moderate', 'sessions': 'flexible', 'preferred_types': ['moderate_activity', 'light_walks'] }, 'MODERATE LOW': { 'min': 30, 'ideal': 45, 'max': 70, 'intensity': 'light_moderate', 'sessions': 'flexible', 'preferred_types': ['light_walks', 'moderate_activity'] }, 'LOW': { 'min': 15, 'ideal': 30, 'max': 45, 'intensity': 'light', 'sessions': 'single', 'preferred_types': ['light_walks'] } } # 獲取品種的運動需求配置 breed_level = exercise_levels.get(breed_needs.upper(), exercise_levels['MODERATE']) # 計算時間匹配度（使用更平滑的評分曲線） if exercise_time >= breed_level['ideal']: if exercise_time > breed_level['max']: # 運動時間過長，適度降分 time_score = 0.15 - (0.08 * (exercise_time - breed_level['max']) / 30) else: time_score = 0.15 elif exercise_time >= breed_level['min']: # 在最小需求和理想需求之間，線性計算分數 time_ratio = (exercise_time - breed_level['min']) / (breed_level['ideal'] - breed_level['min']) time_score = 0.05 + (time_ratio * 0.10) else: # 運動時間不足，根據差距程度扣分 time_ratio = max(0, exercise_time / breed_level['min']) time_score = -0.20 * (1 - time_ratio) # 運動類型匹配度評估 type_score = 0.0 if exercise_type in breed_level['preferred_types']: type_score = 0.05 if exercise_type == breed_level['preferred_types'][0]: type_score = 0.08 # 最佳匹配類型給予更高分數 return max(-0.2, min(0.2, time_score + type_score)) def calculate_grooming_score(breed_needs: str, user_commitment: str, breed_size: str) -> float: """ 計算美容需求分數，強化美容維護需求與使用者承諾度的匹配評估。 這個函數特別注意品種大小對美容工作的影響，以及不同程度的美容需求對時間投入的要求。 """ # 重新設計基礎分數矩陣，讓美容需求的差異更加明顯 base_scores = { "High": { "low": 0.20, # 高需求對低承諾極不合適，顯著降低初始分數 "medium": 0.65, # 中等承諾仍有挑戰 "high": 1.0 # 高承諾最適合 }, "Moderate": { "low": 0.45, # 中等需求對低承諾有困難 "medium": 0.85, # 較好的匹配 "high": 0.95 # 高承諾會有餘力 }, "Low": { "low": 0.90, # 低需求對低承諾很合適 "medium": 0.85, # 略微降低以反映可能過度投入 "high": 0.80 # 可能造成資源浪費 } } # 取得基礎分數 base_score = base_scores.get(breed_needs, base_scores["Moderate"])[user_commitment] # 根據品種大小調整美容工作量 size_adjustments = { "Giant": { "low": -0.35, # 大型犬的美容工作量顯著增加 "medium": -0.20, "high": -0.10 }, "Large": { "low": -0.25, "medium": -0.15, "high": -0.05 }, "Medium": { "low": -0.15, "medium": -0.10, "high": 0 }, "Small": { "low": -0.10, "medium": -0.05, "high": 0 } } # 應用體型調整 size_adjustment = size_adjustments.get(breed_size, size_adjustments["Medium"])[user_commitment] current_score = base_score + size_adjustment # 特殊毛髮類型的額外調整 def get_coat_adjustment(breed_description: str, commitment: str) -> float: """ 評估特殊毛髮類型所需的額外維護工作 """ adjustments = 0 # 長毛品種需要更多維護 if 'long coat' in breed_description.lower(): coat_penalties = { 'low': -0.20, 'medium': -0.15, 'high': -0.05 } adjustments += coat_penalties[commitment] # 雙層毛的品種掉毛量更大 if 'double coat' in breed_description.lower(): double_coat_penalties = { 'low': -0.15, 'medium': -0.10, 'high': -0.05 } adjustments += double_coat_penalties[commitment] # 捲毛品種需要定期專業修剪 if 'curly' in breed_description.lower(): curly_penalties = { 'low': -0.15, 'medium': -0.10, 'high': -0.05 } adjustments += curly_penalties[commitment] return adjustments # 季節性考量 def get_seasonal_adjustment(breed_description: str, commitment: str) -> float: """ 評估季節性掉毛對美容需求的影響 """ if 'seasonal shedding' in breed_description.lower(): seasonal_penalties = { 'low': -0.15, 'medium': -0.10, 'high': -0.05 } return seasonal_penalties[commitment] return 0 # 專業美容需求評估 def get_professional_grooming_adjustment(breed_description: str, commitment: str) -> float: """ 評估需要專業美容服務的影響 """ if 'professional grooming' in breed_description.lower(): grooming_penalties = { 'low': -0.20, 'medium': -0.15, 'high': -0.05 } return grooming_penalties[commitment] return 0 # 應用所有額外調整 # 由於這些是示例調整，實際使用時需要根據品種描述信息進行調整 coat_adjustment = get_coat_adjustment("", user_commitment) seasonal_adjustment = get_seasonal_adjustment("", user_commitment) professional_adjustment = get_professional_grooming_adjustment("", user_commitment) final_score = current_score + coat_adjustment + seasonal_adjustment + professional_adjustment # 確保分數在有意義的範圍內，但允許更大的差異 return max(0.1, min(1.0, final_score)) def calculate_experience_score(care_level: str, user_experience: str, temperament: str) -> float: """ 計算使用者經驗與品種需求的匹配分數，加強經驗等級的影響力 重要改進： 1. 擴大基礎分數差異 2. 加重困難特徵的懲罰 3. 更細緻的品種特性評估 """ # 基礎分數矩陣 - 大幅擴大不同經驗等級的分數差異 base_scores = { "High": { "beginner": 0.15, # 降低起始分，高難度品種對新手幾乎不推薦 "intermediate": 0.65, # 中級玩家仍需謹慎 "advanced": 1.0 # 資深者能完全勝任 }, "Moderate": { "beginner": 0.40, # 適中難度對新手仍具挑戰 "intermediate": 0.85, # 中級玩家較適合 "advanced": 0.95 # 資深者完全勝任 }, "Low": { "beginner": 0.85, # 新手友善品種 "intermediate": 0.90, # 中級玩家幾乎完全勝任 "advanced": 0.85 # 資深者完全勝任 } } # 取得基礎分數 score = base_scores.get(care_level, base_scores["Moderate"])[user_experience] temperament_lower = temperament.lower() temperament_adjustments = 0.0 # 根據經驗等級設定不同的特徵評估標準 if user_experience == "beginner": # 新手不適合的特徵 - 更嚴格的懲罰 difficult_traits = { 'stubborn': -0.30, # 固執性格嚴重影響新手 'independent': -0.25, # 獨立性高的品種不適合新手 'dominant': -0.25, # 支配性強的品種需要經驗處理 'strong-willed': -0.20, # 強勢性格需要技巧管理 'protective': -0.20, # 保護性強需要適當訓練 'aloof': -0.15, # 冷漠性格需要耐心培養 'energetic': -0.15, # 活潑好動需要經驗引導 'aggressive': -0.35 # 攻擊傾向極不適合新手 } # 新手友善的特徵 - 適度的獎勵 easy_traits = { 'gentle': 0.05, # 溫和性格適合新手 'friendly': 0.05, # 友善性格容易相處 'eager to please': 0.08, # 願意服從較容易訓練 'patient': 0.05, # 耐心的特質有助於建立關係 'adaptable': 0.05, # 適應性強較容易照顧 'calm': 0.06 # 冷靜的性格較好掌握 } # 計算特徵調整 for trait, penalty in difficult_traits.items(): if trait in temperament_lower: temperament_adjustments += penalty for trait, bonus in easy_traits.items(): if trait in temperament_lower: temperament_adjustments += bonus # 品種類型特殊評估 if 'terrier' in temperament_lower: temperament_adjustments -= 0.20 # 梗類犬種通常不適合新手 elif 'working' in temperament_lower: temperament_adjustments -= 0.25 # 工作犬需要經驗豐富的主人 elif 'guard' in temperament_lower: temperament_adjustments -= 0.25 # 護衛犬需要專業訓練 elif user_experience == "intermediate": # 中級玩家的特徵評估 moderate_traits = { 'stubborn': -0.15, # 仍然需要注意，但懲罰較輕 'independent': -0.10, 'intelligent': 0.08, # 聰明的特質可以好好發揮 'athletic': 0.06, # 運動能力可以適當訓練 'versatile': 0.07, # 多功能性可以開發 'protective': -0.08 # 保護性仍需注意 } for trait, adjustment in moderate_traits.items(): if trait in temperament_lower: temperament_adjustments += adjustment else: # advanced # 資深玩家能夠應對挑戰性特徵 advanced_traits = { 'stubborn': 0.05, # 困難特徵反而成為優勢 'independent': 0.05, 'intelligent': 0.10, 'protective': 0.05, 'strong-willed': 0.05 } for trait, bonus in advanced_traits.items(): if trait in temperament_lower: temperament_adjustments += bonus # 確保最終分數範圍更大，讓差異更明顯 final_score = max(0.05, min(1.0, score + temperament_adjustments)) return final_score def calculate_health_score(breed_name: str, user_prefs: UserPreferences) -> float: """ 計算品種健康分數，加強健康問題的影響力和與使用者敏感度的連結 重要改進： 1. 根據使用者的健康敏感度調整分數 2. 更嚴格的健康問題評估 3. 考慮多重健康問題的累積效應 4. 加入遺傳疾病的特別考量 """ if breed_name not in breed_health_info: return 0.5 health_notes = breed_health_info[breed_name]['health_notes'].lower() # 嚴重健康問題 - 加重扣分 severe_conditions = { 'hip dysplasia': -0.25, # 髖關節發育不良，影響生活品質 'heart disease': -0.25, # 心臟疾病，需要長期治療 'progressive retinal atrophy': -0.20, # 進行性視網膜萎縮，導致失明 'bloat': -0.22, # 胃扭轉，致命風險 'epilepsy': -0.20, # 癲癇，需要長期藥物控制 'degenerative myelopathy': -0.20, # 脊髓退化，影響行動能力 'von willebrand disease': -0.18 # 血液凝固障礙 } # 中度健康問題 - 適度扣分 moderate_conditions = { 'allergies': -0.12, # 過敏問題，需要持續關注 'eye problems': -0.15, # 眼睛問題，可能需要手術 'joint problems': -0.15, # 關節問題，影響運動能力 'hypothyroidism': -0.12, # 甲狀腺功能低下，需要藥物治療 'ear infections': -0.10, # 耳道感染，需要定期清理 'skin issues': -0.12 # 皮膚問題，需要特殊護理 } # 輕微健康問題 - 輕微扣分 minor_conditions = { 'dental issues': -0.08, # 牙齒問題，需要定期護理 'weight gain tendency': -0.08, # 易胖體質，需要控制飲食 'minor allergies': -0.06, # 輕微過敏，可控制 'seasonal allergies': -0.06 # 季節性過敏 } # 計算基礎健康分數 health_score = 1.0 # 健康問題累積效應計算 condition_counts = { 'severe': 0, 'moderate': 0, 'minor': 0 } # 計算各等級健康問題的數量和影響 for condition, penalty in severe_conditions.items(): if condition in health_notes: health_score += penalty condition_counts['severe'] += 1 for condition, penalty in moderate_conditions.items(): if condition in health_notes: health_score += penalty condition_counts['moderate'] += 1 for condition, penalty in minor_conditions.items(): if condition in health_notes: health_score += penalty condition_counts['minor'] += 1 # 多重問題的額外懲罰（累積效應） if condition_counts['severe'] > 1: health_score *= (0.85 ** (condition_counts['severe'] - 1)) if condition_counts['moderate'] > 2: health_score *= (0.90 ** (condition_counts['moderate'] - 2)) # 根據使用者健康敏感度調整分數 sensitivity_multipliers = { 'low': 1.1, # 較不在意健康問題 'medium': 1.0, # 標準評估 'high': 0.85 # 非常注重健康問題 } health_score *= sensitivity_multipliers.get(user_prefs.health_sensitivity, 1.0) # 壽命影響評估 try: lifespan = breed_health_info[breed_name].get('average_lifespan', '10-12') years = float(lifespan.split('-')[0]) if years < 8: health_score *= 0.85 # 短壽命顯著降低分數 elif years < 10: health_score *= 0.92 # 較短壽命輕微降低分數 elif years > 13: health_score *= 1.1 # 長壽命適度加分 except: pass # 特殊健康優勢 if 'generally healthy' in health_notes or 'hardy breed' in health_notes: health_score *= 1.15 elif 'robust health' in health_notes or 'few health issues' in health_notes: health_score *= 1.1 # 確保分數在合理範圍內，但允許更大的分數差異 return max(0.1, min(1.0, health_score)) def calculate_noise_score(breed_name: str, user_prefs: UserPreferences) -> float: """ 計算品種噪音分數，特別加強噪音程度與生活環境的關聯性評估 """ if breed_name not in breed_noise_info: return 0.5 noise_info = breed_noise_info[breed_name] noise_level = noise_info['noise_level'].lower() noise_notes = noise_info['noise_notes'].lower() # 重新設計基礎噪音分數矩陣，考慮不同情境下的接受度 base_scores = { 'low': { 'low': 1.0, # 安靜的狗對低容忍完美匹配 'medium': 0.95, # 安靜的狗對一般容忍很好 'high': 0.90 # 安靜的狗對高容忍當然可以 }, 'medium': { 'low': 0.60, # 一般吠叫對低容忍較困難 'medium': 0.90, # 一般吠叫對一般容忍可接受 'high': 0.95 # 一般吠叫對高容忍很好 }, 'high': { 'low': 0.25, # 愛叫的狗對低容忍極不適合 'medium': 0.65, # 愛叫的狗對一般容忍有挑戰 'high': 0.90 # 愛叫的狗對高容忍可以接受 }, 'varies': { 'low': 0.50, # 不確定的情況對低容忍風險較大 'medium': 0.75, # 不確定的情況對一般容忍可嘗試 'high': 0.85 # 不確定的情況對高容忍問題較小 } } # 取得基礎分數 base_score = base_scores.get(noise_level, {'low': 0.6, 'medium': 0.75, 'high': 0.85})[user_prefs.noise_tolerance] # 吠叫原因評估，根據環境調整懲罰程度 barking_penalties = { 'separation anxiety': { 'apartment': -0.30, # 在公寓對鄰居影響更大 'house_small': -0.25, 'house_large': -0.20 }, 'excessive barking': { 'apartment': -0.25, 'house_small': -0.20, 'house_large': -0.15 }, 'territorial': { 'apartment': -0.20, # 在公寓更容易被觸發 'house_small': -0.15, 'house_large': -0.10 }, 'alert barking': { 'apartment': -0.15, # 公寓環境刺激較多 'house_small': -0.10, 'house_large': -0.08 }, 'attention seeking': { 'apartment': -0.15, 'house_small': -0.12, 'house_large': -0.10 } } # 計算環境相關的吠叫懲罰 living_space = user_prefs.living_space barking_penalty = 0 for trigger, penalties in barking_penalties.items(): if trigger in noise_notes: barking_penalty += penalties.get(living_space, -0.15) # 特殊情況評估 special_adjustments = 0 if user_prefs.has_children: # 孩童年齡相關調整 child_age_adjustments = { 'toddler': { 'high': -0.20, # 幼童對吵鬧更敏感 'medium': -0.15, 'low': -0.05 }, 'school_age': { 'high': -0.15, 'medium': -0.10, 'low': -0.05 }, 'teenager': { 'high': -0.10, 'medium': -0.05, 'low': -0.02 } } # 根據孩童年齡和噪音等級調整 age_adj = child_age_adjustments.get(user_prefs.children_age, child_age_adjustments['school_age']) special_adjustments += age_adj.get(noise_level, -0.10) # 訓練性補償評估 trainability_bonus = 0 if 'responds well to training' in noise_notes: trainability_bonus = 0.12 elif 'can be trained' in noise_notes: trainability_bonus = 0.08 elif 'difficult to train' in noise_notes: trainability_bonus = 0.02 # 夜間吠叫特別考量 if 'night barking' in noise_notes or 'howls' in noise_notes: if user_prefs.living_space == 'apartment': special_adjustments -= 0.15 elif user_prefs.living_space == 'house_small': special_adjustments -= 0.10 else: special_adjustments -= 0.05 # 計算最終分數，確保更大的分數範圍 final_score = base_score + barking_penalty + special_adjustments + trainability_bonus return max(0.1, min(1.0, final_score)) # 1. 計算基礎分數 print("\n=== 開始計算品種相容性分數 ===") print(f"處理品種: {breed_info.get('Breed', 'Unknown')}") print(f"品種信息: {breed_info}") print(f"使用者偏好: {vars(user_prefs)}") # 計算所有基礎分數並整合到字典中 scores = { 'space': calculate_space_score( breed_info['Size'], user_prefs.living_space, user_prefs.yard_access != 'no_yard', breed_info.get('Exercise Needs', 'Moderate') ), 'exercise': calculate_exercise_score( breed_info.get('Exercise Needs', 'Moderate'), user_prefs.exercise_time, user_prefs.exercise_type ), 'grooming': calculate_grooming_score( breed_info.get('Grooming Needs', 'Moderate'), user_prefs.grooming_commitment.lower(), breed_info['Size'] ), 'experience': calculate_experience_score( breed_info.get('Care Level', 'Moderate'), user_prefs.experience_level, breed_info.get('Temperament', '') ), 'health': calculate_health_score( breed_info.get('Breed', ''), user_prefs ), 'noise': calculate_noise_score( breed_info.get('Breed', ''), user_prefs ) } final_score = calculate_breed_compatibility_score( scores=scores, user_prefs=user_prefs, breed_info=breed_info ) # 計算環境適應性加成 adaptability_bonus = calculate_environmental_fit(breed_info, user_prefs) # 整合最終分數和加成 final_score = (final_score * 0.9) + (adaptability_bonus * 0.1) final_score = amplify_score_extreme(final_score) # 更新並返回完整的評分結果 scores.update({ 'overall': final_score, 'adaptability_bonus': adaptability_bonus }) return scores except Exception as e: print(f"\n!!!!! 發生嚴重錯誤 !!!!!") print(f"錯誤類型: {type(e).__name__}") print(f"錯誤訊息: {str(e)}") print(f"完整錯誤追蹤:") print(traceback.format_exc()) return {k: 0.6 for k in ['space', 'exercise', 'grooming', 'experience', 'health', 'noise', 'overall']} def calculate_environmental_fit(breed_info: dict, user_prefs: UserPreferences) -> float: """計算品種與環境的適應性加成""" adaptability_score = 0.0 description = breed_info.get('Description', '').lower() temperament = breed_info.get('Temperament', '').lower() # 環境適應性評估 if user_prefs.living_space == 'apartment': if 'adaptable' in temperament or 'apartment' in description: adaptability_score += 0.1 if breed_info.get('Size') == 'Small': adaptability_score += 0.05 elif user_prefs.living_space == 'house_large': if 'active' in temperament or 'energetic' in description: adaptability_score += 0.1 # 氣候適應性 if user_prefs.climate in description or user_prefs.climate in temperament: adaptability_score += 0.05 return min(0.2, adaptability_score) def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreferences, breed_info: dict) -> float: """ 改進的品種相容性評分系統 通過更細緻的特徵評估和動態權重調整，自然產生分數差異 """ # 評估關鍵特徵的匹配度，使用更極端的調整係數 def evaluate_key_features(): # 空間適配性評估 space_multiplier = 1.0 if user_prefs.living_space == 'apartment': if breed_info['Size'] == 'Giant': space_multiplier = 0.3 # 嚴重不適合 elif breed_info['Size'] == 'Large': space_multiplier = 0.4 # 明顯不適合 elif breed_info['Size'] == 'Small': space_multiplier = 1.4 # 明顯優勢 # 運動需求評估 exercise_multiplier = 1.0 exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper() if exercise_needs == 'VERY HIGH': if user_prefs.exercise_time < 60: exercise_multiplier = 0.3 # 嚴重不足 elif user_prefs.exercise_time > 150: exercise_multiplier = 1.5 # 完美匹配 elif exercise_needs == 'LOW' and user_prefs.exercise_time > 150: exercise_multiplier = 0.5 # 運動過度 return space_multiplier, exercise_multiplier # 計算經驗匹配度 def evaluate_experience(): exp_multiplier = 1.0 care_level = breed_info.get('Care Level', 'MODERATE') if care_level == 'High': if user_prefs.experience_level == 'beginner': exp_multiplier = 0.4 elif user_prefs.experience_level == 'advanced': exp_multiplier = 1.3 elif care_level == 'Low': if user_prefs.experience_level == 'advanced': exp_multiplier = 0.9 # 略微降低評分，因為可能不夠有挑戰性 return exp_multiplier # 取得特徵調整係數 space_mult, exercise_mult = evaluate_key_features() exp_mult = evaluate_experience() # 調整基礎分數 adjusted_scores = { 'space': scores['space'] * space_mult, 'exercise': scores['exercise'] * exercise_mult, 'experience': scores['experience'] * exp_mult, 'grooming': scores['grooming'], 'health': scores['health'], 'noise': scores['noise'] } # 計算加權平均，關鍵特徵佔更大權重 weights = { 'space': 0.35, 'exercise': 0.30, 'experience': 0.20, 'grooming': 0.15, 'health': 0.10, 'noise': 0.10 } # 動態調整權重 if user_prefs.has_children: if user_prefs.children_age == 'toddler': weights['noise'] *= 1.5 # 幼童對噪音更敏感 weights['experience'] *= 1.3 # 需要更有經驗的飼主 if user_prefs.living_space == 'apartment': weights['space'] *= 1.4 # 公寓空間限制更重要 weights['noise'] *= 1.3 # 噪音問題更重要 # 運動時間極端情況 if user_prefs.exercise_time < 30: weights['exercise'] *= 1.5 # 運動時間極少時加重權重 elif user_prefs.exercise_time > 150: weights['exercise'] *= 1.3 # 運動時間充足時略微加重 # 正規化權重 total_weight = sum(weights.values()) normalized_weights = {k: v/total_weight for k, v in weights.items()} # 計算最終分數 final_score = sum(adjusted_scores[k] * normalized_weights[k] for k in scores.keys()) # 品種特性加成 breed_bonus = calculate_breed_bonus(breed_info, user_prefs) # 整合最終分數，保持在0-1範圍內 return min(1.0, max(0.0, (final_score * 0.85) + (breed_bonus * 0.15))) def amplify_score_extreme(score: float) -> float: """ 改進的分數轉換函數，提供更大的分數區間和更明顯的差異 轉換邏輯： - 極差匹配 (0.0-0.2) -> 50-60% - 較差匹配 (0.2-0.4) -> 60-70% - 中等匹配 (0.4-0.6) -> 70-82% - 良好匹配 (0.6-0.8) -> 82-90% - 優秀匹配 (0.8-1.0) -> 90-98% """ if score < 0.2: # 極差匹配：更低的起始分數 return 0.50 + (score / 0.2) * 0.10 elif score < 0.4: # 較差匹配：緩慢增長 position = (score - 0.2) / 0.2 return 0.60 + position * 0.10 elif score < 0.6: # 中等匹配：較大的分數增長 position = (score - 0.4) / 0.2 return 0.70 + position * 0.12 elif score < 0.8: # 良好匹配：快速增長 position = (score - 0.6) / 0.2 return 0.82 + position * 0.08 else: # 優秀匹配：達到更高分數 position = (score - 0.8) / 0.2 return 0.90 + position * 0.08